Application Of Geostatistics Research Articles

Spatial Heterogeneity Analysis and Risk Assessment of Potentially Toxic Elements in Soils of Typical Green Tea Plantations

The spatial heterogeneity of potentially toxic elements (PTEs) in a typical green tea-producing area in Zhejiang was investigated with application of geostatistics. The positive matrix factorization (PMF) was conducted for analysis of pollution sources and risk assessment of the soil of the tea garden. The results revealed that 93.52% of the study area did not exceed the PTEs risk screening value in the soil pollution risk control standard of agricultural land. The results of the spatial heterogeneity analysis showed that Cd and Pb had moderate spatial auto-correlation, exhibiting similar spatial distribution patterns. The high-value locations were distributed in the southeast of the study area, while low-value locations were distributed in the southwest of the study area. The Cr, As, and Hg had strong spatial auto-correlation, while Cr and As had similar spatial distribution patterns whose high-value areas and low-value areas were concentrated in the west and center of the study area, respectively. The Cd, Pb, and As originated from the agricultural source, transportation source, and industrial source, respectively, while Cr and Hg were from the natural source on the basis of the results of the PMF model. The results of a potential ecological risk assessment revealed that five PTEs in the study area were of low potential risk. The single-factor ecological risk ranking was Cd > As > Hg > Cr > Pb. The overall ecological risk in the study area was slight. The human health risk model indicates that there was a non-carcinogenic risk for children in the study area, and the high-value area was concentrated in the northwest of the study area. It is concluded that emphasis shall be given to excessive Cd caused by agricultural sources in the southeast of the study area, and control and monitoring will be strengthened in the northwestern part of the study area. The relevant measures for prevention of soil pollution must be conducted.

Geostatistics and GIS as a Technique for Chemical Soil Properties Mapping

Soil properties play a fundamental role in agriculture, and understanding their spatial distribution is essential for effective land management and increasing agricultural productivity. This study focuses on the application of geostatistics and Geographic Information Systems (GIS) as techniques for mapping soil properties, in the case of the Mozambique Sugar cane company (ADM) in Mafambisse, in the province of Sofala, Mozambique. The central objective is to demonstrate how geostatistics and GIS can be used to characterize and map soil properties. The research was materialized using field surveys that estimated the pH in the field of several sampling points at different depths in the irrigated perimeter of the ADM. Geostatistics is applied, using methods such as kriging and variogram analysis, allowing the creation of continuous maps that represent soil properties in unsampled locations. GIS is an essential tool for integrating geostatistical data and creating accessible and informative maps. The results of this study indicate that the combination of geostatistics and GIS offers an effective approach for mapping soil properties. This knowledge is crucial for Mozambique sugar cane company, an important company in the sugar industry, to optimize its agricultural practices, increase productivity and minimize environmental impacts. Thus, offering solid bases for making informed and sustainable agricultural decisions, contributing to efficient agricultural development in Mozambique and other regions with similar challenges.

A landscape in turmoil: characterizing the multi-decadal growth trajectory of brackishwater aquaculture in Medinipur Coastal Plain, India

The spatio-temporal growth trajectory of coastal brackishwater aquaculture (CBA) in the coastal tracts of Purba Medinipur, India and its impending repercussions on the immediate environs, was primarily assessed in this study through application of geostatistics, landscape metrics, and geospatial technologies. Three Community Development (CD) Blocks, namely, Contai-I, Deshapran, and Ramnagar-II were considered to analyze the growth pattern of the area under CBA. Landsat datasets of the assessment years (1991, 2001, 2011, 2021, and 2023) were used to prepare the land use/ land cover (LULC) maps and to derive pertinent landscape metrics of patch and class levels for this region. This brought forward a highly fragmented and dispersive spatial concentration of the CBA farms in the entire study area. Additionally, Census Village-wise growth pattern of CBA was analyzed by conducting a spatial autocorrelation analysis which depicted prominent clustering of villages with a higher concentration of CBA. Results showed that there has been an incessant growth of CBA in the last three decades, however, a sharp drop has been recorded owing to recurrent bouts of diseases, swelling production costs, and a sharp drop-in market rate. The nature of growth and/ or decay of the CBA was predicted for the year 2025 using Cellular Automata and Artificial Neural Network (CA-ANN) model. After careful calibration and validation, the model projected further lessening of the CBA area along with a continued expansion of abandoned aquaculture. Accordingly, ecologically viable livelihood alternatives and environmentally sustainable management measures were suggested for the efficient monitoring of these highly fragile tropical ecosystems.

Groundwater Contamination in a Rural Municipality of Northeastern Brazil: Application of Geostatistics, Geoprocessing, and Geochemistry Techniques

Groundwater Contamination in a Rural Municipality of Northeastern Brazil: Application of Geostatistics, Geoprocessing, and Geochemistry Techniques

Mapeando a Variabilidade das Chuvas na Bacia do Rio São Francisco: Insights para a Gestão de Recursos Hídricos

Abstract The São Francisco River Basin (SFRB) is extremely important for both the local rural and urban population, running through several states and municipalities in Brazil. Thus, the study of the spatial distribution of rainfall in the SFRB, supported by the application of geostatistics, offers valuable insights for water management and planning. In this study, data from 341 rainfall stations were used, corresponding to the period between 1989 and 2018, and monthly accumulated rainfall analyzed. About the trends, positive estimates for latitude, except for April, highlight that the accumulated precipitation decreases from south to north each month. In December, 85.10% of the spatial variability of rainfall was explained by the trend of rainfall concerning geographical coordinates. Concerning spatial dependence, the analyzed data predominantly exhibited the exponential variogram model. The conducted analyses offered a holistic view of rainfall in the region, facilitating the understanding of spatial patterns and shedding light on challenges in the face of climate change. Our research limited on the spatial modeling of rainfall in the SFRB, and future work should include modeling socio-economic and environmental impacts in the region concerning the spatial irregularity of rainfall.

Large-scale precipitation monitoring network re-design using ground and satellite datasets: coupled application of geostatistics and meta-heuristic optimization algorithms

Large-scale precipitation monitoring network re-design using ground and satellite datasets: coupled application of geostatistics and meta-heuristic optimization algorithms

Spatiotemporal Analysis of Rainfall and Droughts in a Semiarid Basin of Brazil: Land Use and Land Cover Dynamics

Precipitation estimation is a challenging task, especially in regions where its spatial distribution is irregular and highly variable. This study evaluated the spatial distribution of annual rainfall in a semiarid Brazilian basin under different regimes and its impact on land use and land cover dynamics. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) records and observed data from 40 weather stations over a time series of 55 years were used, in addition to the Standardized Precipitation Index. Spatiotemporal analysis was carried out based on geostatistics. Remote sensing images were also interpreted for different rainfall regimes using the Normalized Difference Vegetation Index and Modified Normalized Difference Water Index. The Gaussian semivariogram model best represented the rainfall spatial structure, showing strong spatial dependence. Results indicated that rainfall amount in the basin significantly increases with elevation, as expected. There is high variation in the dynamics of water storage that can threaten water security in the region. Our findings point out that the application of geostatistics for mapping both the annual precipitation and the Standardized Precipitation Index provides a powerful framework to support hydrological analysis, as well as land use and land cover management in semiarid regions.

Spatial variability of the productive capacity of teak (Tectona grandis Linn F.) plantations in the eastern Amazonia

Application of geostatistics in mapping the productive capacity of forest stands is an important tool to guide decision making regarding precision silviculture. In this study, we investigated the productive capacity of teak clonal plantations and its spatial dependence, making the site mapping possible. We also investigated the influence of soil chemical attributes on productive potential. We used dominant height, age and geographic coordinates data from plots during five evaluations in clonal teak plantations of different ages. We performed the classification of the forest sites by the guide-curve method. The principal components analysis was used to group the forest stands according to soil chemical attributes (available P and exchangeable K, Na and Mg). The spatial variability analysis of productive capacity was carried out through geoestatistics. The results show that the productive capacity has spatial dependence. The exponential model has provided the best estimates of dominant height, revealing that 69.2% of the mapped area was from medium and 30.8% from high productive capacity. The multivariate analysis showed that soil fertility has influenced the productive capacity, discerning the most productive sites, in agreement with the soil quality. The geostatistical technique was efficient to estimate the productive capacity in unsampled areas, revealing sites with different productive potentials and their response to edaphic factors. This may assist in the efficient management of planting, when establishing silvicultural actions in specific locations, which are identified in the planted area maps, reducing the management costs.

Determination Of Relative Error in Coal Resource Classification Based On Geostatistical Drill Hole Spacing Analysis: A Case Study of Coal Deposits at Batang Hari, Jambi

Generally, the determination of resource classification is only qualitative based on the geometric factors and geological complexity that control it. However, as the prospect area is found to have a reasonably heterogeneous sediment characteristic, a method is needed that can be used to increase the level of confidence in determining the Optimum Drill Hole Spacing. Therefore, this study uses the application of geostatistics with the global estimation variance (GEV) method based on the relative error value of each parameter, namely the thickness geometry and quality in the form of Ash and VM. This research was carried out in Jangga Aur village, Bathin XXIV District, Batang Hari Regency, Jambi Province, Working Area of PT Berkat Bara Persada Jobsite PT Inti Bara Nusalima.The Drill Hole Spacing Analysis (DHSA) results will obtain optimum spacing on resource classification based on relative error values, namely 0-10% for measured resources, 10-20% for indicated resources, and 20% for Inferred resources carried out the on-seam reference. Based on the results of the study, it was found that the seam reference used was Seam D, then the spacing distance of the drill hole on the coal seam of the research area, which had an average distance of 80 m, with geostatic analysis could be increased up to a distance of 250 m in measured resources, indicated resources of 450 m and Inferred 800 m.

Geostatistical Inversion of Spectrally Broadened Seismic Data for Re-Evaluation of Oil Reservoir Continuity in Inas Field, Offshore Malay Basin

The application of geostatistics in seismic inversion techniques has been proven somewhat reliable in the delineation of reservoir properties and has recently attracted the attention of many geoscientists. However, there are cases where its prediction returned negative results after drilling. In this research, we re-evaluated a reservoir in Inas Field, whose geostatistical inversion result wrongly predicted sand continuity, resulting in the spudding of a dry hole. When a geostatistical seismic inversion is successfully applied, it provides an increase in seismic resolution and aids the prediction of sand continuity. Although this method relies more on the statistical data from a well because of the limitation of the seismic data in resolving thin geologic features, the spatial variation of reservoir parameters still depends on seismic data, which often have poor resolution quality. Therefore, to investigate the impact of bandlimited data on the geostatistical inversion, we harmonically extended the seismic bandwidth by applying a sparse-layer spectral inversion algorithm to the data. This algorithm increased the seismic data bandwidth from 80 Hz to 180 Hz, and its tuning thickness reduced from 32 m to 10 m at the reservoir interval. The resultant broadband (180 Hz), as well as the original seismic (narrowband of 80 Hz) data, were both used as input to build two separate geostatistical prediction models, respectively. Twenty (20) realizations of these models were generated, ranked into P10, P50, and P90, and the best case was selected for interpretation. These realizations were used to characterize the reservoir lithofacies distribution. When compared, the result of the broadband inversion, facies and sand distribution model showed that the reservoir facies changed towards the location of the dry well. The broadband geostatistical inversion efficiently improved the reservoir characterization process by not only producing an accurate estimation of the lateral extent of the reservoir heterogeneities but also generating outcomes that help us understand why other geostatistical inversion analyses of the target reservoir were misleading. Contrary to the popular assumption, it was discovered that the tuning effects of bandlimited data could affect the result of a geostatistical inversion and result in wrong facies predictions.

GSTools v1.3: a toolbox for geostatistical modelling in Python

Abstract. Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.

SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python

Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e., for interpolation, rescaling, data assimilation or modeling. At its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations. The variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method. Unfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram. Not least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user. This is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all. Furthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied. SciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow. Its main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python. During the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat. Today, the most important ones can be interfaced by SciKit-GStat. Additionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat. Common data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms. Common approaches to estimate variograms are covered and can be used out of the box. At the same time, the base class is very flexible and can be adjusted to less common problems, as well. Last but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases. With broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.

On a software tool for the practical application of Geostatistics in Geology and Geotechnics

On a software tool for the practical application of Geostatistics in Geology and Geotechnics

Model Selection for Mineral Resource Assessment Considering Geological and Grade Uncertainties: Application of Multiple-Point Geostatistics and a Cluster Analysis to an Iron Deposit

The assessment of mineral resources requires the definition of a geological domain, and the quantitative results obtained should consider geological and grade uncertainties. However, when the available data are limited, it can be difficult to estimate the shape of an ore body owing to the spatial uncertainty. This study describes a methodology for defining geological domains using multiple-point geostatistics and selecting models for realizations using a cluster analysis. Then, the selected realizations were assessed for mineral resources using two-point geostatistics. Of the various multiple-point geostatistics algorithms, the single normal equation simulation was used to generate a realistic ore body, and various cluster algorithms, such as k-means and a density-based spatial clustering of applications with noise, were applied to select a model. Based on the outcomes, a mineral resource assessment that uses a sequential Gaussian simulation was performed, and a reasonable model that reflects the original data well was selected based on the grade statistics of the original data and mineral resource assessment per cluster. This study demonstrates that it is possible to define the ore domain and assess mineral resources while accounting for the uncertainty caused by a lack of spatial information.

GEOSTATISTICAL MODELLING OF RESERVOIR QUALITY OVER'BRIGHT' FIELD,NIGER DELTA.

The quality of any hydrocarbon-bearing reservoir is vital for a successful exploitation work.. The reservoir quality is a function of its petrophysical parameters. Hence the need to model these properties geostatistically in order to determine the quality away from well locations.Composite logs for four wells and 3-D seismic data were used for the analysis. A reservoir named Sand X was mapped and correlated across wells 1 through 4. The four reservoir quality indicators - Effective porosity, permeability, volume of shale and net-to-gross- were estimated and modelled across the field. Sequential Gaussian simulation algorithm was employed to distribute these properties stochastically away from well locations and five realizations were generated. The volume of shale varied from 0.025 (Well 1, second realization) to 0.18(Well 2, first realization). The net-to-gross varied from 0.81 to 0.96 in wells 3 and 4 respectively, for the third realization, while the effective porosity varied from 0.125 to 0.295 for the fifth realization in Wells 3 and 4 respectively. The permeability is above 5000mD at all the existing well locations.These realizations were ranked using Lp norm statistical tool to pick the best for further evaluation. The reservoir quality deduced from the analyzed indicators was favourably high across the reservoir.The application of geostatistics has laterally enhanced the log data resolution away from established well locations.

Using GIS and Geostatistical Techniques for Mapping Piezometry and Groundwater Quality of the Albian Aquifer of the M’zab Region, Algerian Sahara

The M’zab region is subject to an arid Saharan climate where surface and sub-surface waters are of little importance. The Albian Aquifer, commonly called Continental Intercalary (CI), main component of North Western Sahara Aquifer System (NWSAS/SASS), constitutes the most extensive aquifer formation of the region. In our study area, the CI is identified, as a regional subset, as the Albian Aquifer of M’zab Region (AAMR). Its groundwater resources are considered the only source available to meet the growing needs of drinking water supply, agriculture and industry. This aquifer is heavily exploited by a very large number of wells (more than 750). Its supply is very low, so it is a very low renewable layer. This requires periodic monitoring and control of its piezometric level and its physico- chemical quality. The objective of our study is to know the current state of this aquifer, while studying the variation of its piezometry for the period 2010-2018, and also the chemical quality of its groundwater by analyzing more than 90 samples over the entire study area. The application of geostatistics by kriging and the steps of analysis, modelling and calculation of semivariogram have enabled us to draw up maps of the various hydrogeological and hydrochemical parameters. As a result, twelve thematic maps were gridded using Geostatistical tools of ArcGIS software. The water-level-change map showed a significant drop in the groundwater level over the entire M’zab region and especially around the major cities (Ghardaïa, Berriane, Metlili and Zelfana) with more than 8 meters. Chemical analyses of the Albian groundwater in the study area show the dominance of evaporite facies (Cl--Na+-Ca2+) with low concentrations than the Algerian Standards for Drinking (ASD). All the water quality indices (WQI) that have been mapped reveal that the groundwater samples were suitable for drinking and irrigation with a high quality of water located in the south of the study area.

Geostatistics-block-based characterization of heterogeneous rock mass and its application on ultimate pit limit optimization: a case study

For an open-pit mine, the slope must remain stable throughout the life of mining operation, and it follows that an optimized ultimate pit limit (UPL) should have the slope stability commensurate with economic benefit. In the Shuguang gold and copper mine, a geostatistics-block-based method is used to characterize the heterogeneous mechanical properties of rock mass. Then, the detailed slope stability analyses for four possible slope configuration designs using heterogenous mechanical parameter block model are performed to determine the steepest safe slope angle, and the steepest safe slope angle is next used for the UPL optimization. Compared with the original UPL assuming the rock mass is homogeneous in the same lithology, the slope angle for the optimized UPL has an average 1° to 6° increase, and the optimized UPL can bring 15.84 million tons of ore and reduce 20.83 million tons of waste rock. The result indicated that the application of geostatistics can make practical use of geotechnical information to improve slope stability, and slope configurations, and thereby optimize the UPL and so bring economic benefit.

Risk assessment of forest decline by application of geostatistics and multi-criteria analysis

Abstract In this paper, the risk zone mapping of declining lowland forests belonging to „Morović“, management unit „Varadin-Županja“ (northern Serbia) is performed using geostatistics analysis. Based on the monitoring of groundwater level, the Kriging method has been performed for the spatial distribution of groundwater level for a multiyear period (2010–2013) – reference level and characteristic levels for the wettest and the driest year during the analyzed period. Risk assessment was determined by the variance of characteristics compared to reference levels. Then, multi-criteria decision analysis methods (AHP, PROMETHEE II) were applied to define the rank of each department (smaller forest management units) located in the research area. These analyses are very important because they enable to locate of the area with a high risk of forest decline and to rank departments using criteria: deviation from water level recorded during dry periods, species demand for water, conservation status and purpose of the unit (seed stands or technical wood). The proposed methodology is usable for the determination of the primary localities for the application of management measures conducting on the level of lower planned units (departments) and thus lead to the successful planning and more efficient forest management. Obtained results at the researched area showed that a negative influence on the watering regime has groundwater level decreasing compared to the reference level because it directly affects available water for the plants. Based on multicriteria analysis methods, it was deduced that the most endangered parts are located at the edge, while this risk is much lower in the central part of the management unit. A combination of applied methods (geostatistics and multicriteria analysis) is of great importance for forestry management.

Progress in the Reconstruction of Terrain Relief Before Extraction of Rock Materials—The Case of Liban Quarry, Poland

Open pit mining leads to irreversible changes in topographical relief, which makes a return to the original morphology virtually impossible. This is important for quarries that were part of former mining areas. This research presents an innovative approach to the reconstruction of the relief of anthropogenically transformed land on the example of Liban Quarry in Cracow, where operations began before 1873 to 1986. The basis for the reconstructed area was a Topographic Map of Poland with a scale 1:10,000 from 1997, from which a set of data was obtained to perform spatial analyses. The estimation was conducted using the ordinary kriging method, enabling a reconstruction of the morphology of the studied area and presenting it in the form of a hypsometric map and a digital elevation model. The correctness of the modelling was verified by cross-validation and a kriging standard deviation map (SDOK). These revealed low values of estimation errors in the places without contour lines on the base map. The comparison of the obtained maps and model with a Tactical Map of Poland with a scale 1:100,000 from 1934 indicated great similarities. The highest interpolation error value was recorded in the part of the pit where the difference between the actual and reconstructed elevation was about 30 m on average. In the exploited part, the SDOK did not exceed 0.52 m, and in the entire studied area, it reached a maximum of 0.56 m. The proposed approach fulfilled the assumptions of reconstruction, as the analysis revealed elements matching the historic relief in both forms of presentation of the topography of the quarry, on the obtained hypsometric map and on the tactical map. Our study is among the very few in the world concerning the application of geostatistics in the restoration of the relief of land transformed by open pit mining activities.

Application of geostatistics for grid and random sampling schemes for a grassland in Nigde, Turkey.

Soil quality prediction maps are important tools for environmental scientists and policymakers. An 18ha grassland was selected to create soil quality prediction maps. A total of 30 sampling points were selected, and samples were collected from top soil (0-20cm depth). Twelve of the sampling points were selected randomly and 18 of the sampling points were selected based on a square shaped grid plan. The soil samples were then analyzed for pH, electrical conductivity (EC), organic matter (OM), water content, dissolved total carbon (DTC), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and dissolved total nitrogen (DTN). Ordinary kriging (OK) and ordinary cokriging (OCK) spatial interpolation methods were used for the prediction of spatial distribution. The prediction errors showed that the parameters in the grid sampling scheme showed better prediction in the OK technique. The highest reduction in prediction errors was obtained in the DOC in grid sampling scheme after using OCK.